Autonomous Navigation of Robots Based on the Improved Informed-RRT${}^{\ast }$ Algorithm and DWA

Abstract

An improved method is proposed in this investigation to solve the problems of poor path quality and low navigation efficiency of the Informed-RRT ∗ algorithm in robot autonomous navigation. First, the greedy algorithm is introduced in the path planning procedure. When a new node is obtained, it will be judged whether it can directly reach the target point. Second, the search scope of the potential optimal parent node becomes the constructed path, instead of the node tree, which reduces the number of nodes to be searched and improves the navigation efficiency. Combined with the dynamic window approach (DWA), the improved algorithm is utilized to simulate the autonomous navigation process of the robot based on the Robot Operating System (ROS) platform. The simulation results show that compared with the original algorithm, the length of the global path is reduced by 5.15%, and the time of planning path and autonomous navigation is shortened by 78.34% and 21.67%, respectively.

Recent advances in Rapidly-exploring random tree: A review

“…In Ref. [ 96 ], Informed-RRT* incorporates dynamic window approach further reduces the global path length. To address the safety and productivity of construction sites due to temporal and spatial conflicts, a hybrid algorithm incorporating Informed-RRT*, geometry and discrete event simulation is implemented and evaluated, and experiments show that the proposed algorithm satisfies all types of obstacle avoidance constraints, provides priority for higher priority construction activities, and also obtains the shortest route and time for each transport vehicle [ 97 ].…”

“…From the theoretical level, the feasible solutions can be further optimized in terms of evaluation metrics such as path length, complexity, and consumption time, etc. Some of the previous reviews also falls into post-processing, like Some kinds of RRT-Connect [ 35 , 36 , 45 , 46 ], informed-RRT* [ 23 , 92 , 96 ], Quick-RRT* [ 26 , 110 , 112 , 113 ], RRT*-Smart [ 73 , 79 , 80 ], Fast-RRT* [ 116 , 119 ], F-RRT* [ 7 , 118 ], post triangular rewiring strategy [ 52 , 137 ], interpolation post-processing [ 138 , 139 ]. From the application level, the feasible solutions are likely to not satisfy the kinematic model constraints of the robot or the scenario constraints in some application scenarios, therefore, the theoretically generated feasible solutions cannot be applied to real scenarios and further optimization is still required.…”

Asymptoticcaly-Optimal Path Planning Using the Improved Probabilistic Road Map Algorithm

“…Jiang dkk. [18] mengusulkan adjoined obstacle informed RRT*, Dai dkk [19] mengusulkan kombinasi informed RRT* dengan dynamic window approach [DWA], dan Ryu dan Park [20] mengusulkan penggunaan gridmap skeletonization pada informed RRT*. Adapun Kim dan Song [21] mengusulkan peningkatan kecepatan komputasi dari algoritma informed RRT* dengan menggunakan proses wrapping.…”

Adaptive dynamic windowing approach based on risk degree function

“…Eduardo Molinos et al (2019) proposed an improved DW4DO algorithm based on the traditional DWA algorithm, which improved the defects of the algorithm based on curvature velocity, so that the robot can better carry out path planning in the dynamic environment. Dai et al (2022) introduced greedy algorithm into path planning, combining DWA algorithm with RRT* algorithm to realize autonomous navigation on robot operating platform (ROS).…”